Sparing Healthy Tissue and Increasing Tumor Dose Using Bayesian Modeling of Geometric Uncertainties for Planning Target Volume Personalization

Objective

To develop a mathematical tool that can update a patient's planning target volume (PTV) partway through a course of radiation therapy to more precisely target the tumor for the remainder of treatment and reduce dose to surrounding healthy tissue.

Methods and Materials

Daily on-board imaging was used to collect large datasets of displacements for patients undergoing external beam radiation therapy for solid tumors. Bayesian statistical modeling of these geometric uncertainties was used to optimally trade off between displacement data collected from previously treated patients and the progressively accumulating data from a patient currently partway through treatment, to optimally predict future displacements for that patient. These predictions were used to update the PTV position and margin width for the remainder of treatment, such that the clinical target volume (CTV) was more precisely targeted.

Results

Software simulation of dose to CTV and normal tissue for 2 real prostate displacement datasets consisting of 146 and 290 patients treated with a minimum of 30 fractions each showed that re-evaluating the PTV position and margin width after 8 treatment fractions reduced healthy tissue dose by 19% and 17%, respectively, while maintaining CTV dose.

Conclusion

Incorporating patient-specific displacement patterns from early in a course of treatment allows PTV adaptation for the remainder of treatment. This substantially reduces the dose to healthy tissues and thus can reduce radiation therapy–induced toxicities, improving patient outcomes.

Introduction

Numerous solutions have been proposed to the problem of setting clinical target volume (CTV) to planning target volume (PTV) margins for external beam radiation therapy (EBRT). A comprehensive approach to setting margins must consider numerous factors, including geometric or positional uncertainties and anatomic factors such as inhomogeneities in tissue density and radiosensitivity of surrounding healthy tissue. This work focuses specifically on margins necessitated by geometric considerations. Margin recipes compromise between wide margins, which risk overdosing surrounding healthy tissue, and narrow margins, which risk underdosing the CTV 1, 2, 3, 4, 5. Margins are commonly held constant throughout treatment for all patients. However, recent studies of collected real-world displacement data for patients undergoing EBRT demonstrate that patient random errors vary considerably between patients (6) and that this warrants consideration when margin recipes are being designed to achieve adequate coverage. The variability in random errors necessitates increased margins because margins must be wide enough to cover a reasonable worst case.

This work demonstrates that variability in random errors also makes treatment personalization possible. Margins increase for patients with larger random errors, and they decrease for others. We describe tissue-sparing margin adaptive radiation therapy (T-SMART), a statistical method for implementing margin personalization. Compared with margins that are constant for all patients and wide enough to cover a reasonable worst case, T-SMART achieved reductions of ∼20% in dose to surrounding healthy tissue for 2 large cohorts of patients with cancer.